292 lines
7.0 KiB
INI
292 lines
7.0 KiB
INI
# fa_core_news_trf: the whole pipeline on one fine-tuned ParsBERT encoder.
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#
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# Differences from the sm/md/lg tiers, all forced by the transformer:
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#
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# * One corpus, not two. sm/md/lg train `ner` separately (own embedded tok2vec) and source
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# it into the dep model. Fine-tuning a 162M-parameter encoder twice would double GPU cost
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# and ship two encoders in one wheel, and the second would collide on the `transformer`
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# component name. So every component listens to a single shared transformer and trains
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# against corpus/joint/, built by scripts/merge_joint_corpus.py (UD layer + the
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# difflib-transferred NER layer on identical tokenization).
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# * `use_upper = false` on both transition-based parsers: with a transformer upstream the
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# extra maxout layer is redundant, and this matches the upstream *_trf configs.
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# * Adam + warmup_linear and accumulate_gradient=3, not the flat 0.001 the CPU tiers use.
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# Fine-tuning a pretrained encoder at 1e-3 diverges.
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# * gpu_allocator = "pytorch" so thinc and torch share one CUDA memory pool.
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#
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# Encoder: HooshvareLab/bert-base-parsbert-uncased. NOTE the licence caveat in
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# docs/MODELS.md §3.4 - ParsBERT's model card carries no licence statement, so this wheel is
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# NOT redistributable on those grounds; HooshvareLab/roberta-fa-zwnj-base (Apache-2.0) is the
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# publishable alternative and drops in by changing `name` below.
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[paths]
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train = null
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dev = null
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vectors = null
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init_tok2vec = null
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[system]
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gpu_allocator = "pytorch"
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seed = 0
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[nlp]
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lang = "fa"
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pipeline = ["transformer","tagger","morphologizer","trainable_lemmatizer","parser","ner"]
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batch_size = 128
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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[nlp.tokenizer]
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@tokenizers = "spacy.Tokenizer.v1"
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[nlp.vectors]
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@vectors = "spacy.Vectors.v1"
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[components]
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[components.transformer]
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factory = "transformer"
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max_batch_items = 4096
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[components.transformer.set_extra_annotations]
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@annotation_setters = "spacy-transformers.null_annotation_setter.v1"
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[components.transformer.model]
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@architectures = "spacy-transformers.TransformerModel.v3"
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name = "HooshvareLab/bert-base-parsbert-uncased"
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mixed_precision = false
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[components.transformer.model.get_spans]
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@span_getters = "spacy-transformers.strided_spans.v1"
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window = 128
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stride = 96
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[components.transformer.model.tokenizer_config]
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use_fast = true
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[components.transformer.model.transformer_config]
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[components.transformer.model.grad_scaler_config]
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[components.tagger]
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factory = "tagger"
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label_smoothing = 0.05
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overwrite = false
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neg_prefix = "!"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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normalize = false
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[components.tagger.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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upstream = "*"
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[components.tagger.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.tagger.scorer]
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@scorers = "spacy.tagger_scorer.v1"
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[components.morphologizer]
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factory = "morphologizer"
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label_smoothing = 0.05
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overwrite = true
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extend = false
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[components.morphologizer.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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normalize = false
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[components.morphologizer.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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upstream = "*"
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[components.morphologizer.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.morphologizer.scorer]
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@scorers = "spacy.morphologizer_scorer.v1"
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[components.trainable_lemmatizer]
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factory = "trainable_lemmatizer"
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backoff = "orth"
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min_tree_freq = 3
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overwrite = false
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top_k = 1
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[components.trainable_lemmatizer.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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normalize = false
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[components.trainable_lemmatizer.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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upstream = "*"
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[components.trainable_lemmatizer.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.trainable_lemmatizer.scorer]
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@scorers = "spacy.lemmatizer_scorer.v1"
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[components.parser]
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factory = "parser"
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moves = null
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update_with_oracle_cut_size = 100
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learn_tokens = false
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min_action_freq = 30
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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = false
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nO = null
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[components.parser.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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upstream = "*"
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[components.parser.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.parser.scorer]
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@scorers = "spacy.parser_scorer.v1"
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[components.ner]
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factory = "ner"
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moves = null
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update_with_oracle_cut_size = 100
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incorrect_spans_key = null
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = false
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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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upstream = "*"
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[components.ner.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.ner.scorer]
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@scorers = "spacy.ner_scorer.v1"
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[corpora]
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[training]
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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accumulate_gradient = 3
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# 3000 steps is ~40 epochs over this 445k-token corpus, measured at ~29 steps/min on a T4
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# (~1.8h). The CPU tiers' 20000/1600 would be ~270 epochs and ~12h here, and worse than
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# wasteful: warmup_linear anneals against `total_steps`, so a run stopped early by patience
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# never leaves the peak learning rate. Budget and schedule are kept equal on purpose:
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# training.optimizer.learn_rate.total_steps must track any change to max_steps.
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patience = 600
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max_epochs = 0
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max_steps = 3000
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eval_frequency = 100
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frozen_components = []
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annotating_components = []
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before_to_disk = null
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before_update = null
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[training.optimizer]
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@optimizers = "Adam.v1"
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beta1 = 0.9
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beta2 = 0.999
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L2_is_weight_decay = true
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L2 = 0.01
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grad_clip = 1.0
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use_averages = false
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eps = 1e-08
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[training.optimizer.learn_rate]
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@schedules = "warmup_linear.v1"
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warmup_steps = 250
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total_steps = 3000
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initial_rate = 5e-5
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[training.batcher]
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@batchers = "spacy.batch_by_padded.v1"
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discard_oversize = true
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size = 2000
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buffer = 256
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get_length = null
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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progress_bar = false
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[training.score_weights]
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tag_acc = 0.16
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pos_acc = 0.08
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tag_micro_p = null
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tag_micro_r = null
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tag_micro_f = null
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morph_acc = 0.08
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morph_per_feat = null
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lemma_acc = 0.16
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dep_uas = 0.08
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dep_las = 0.16
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dep_las_per_type = null
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sents_p = null
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sents_r = null
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sents_f = 0.0
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ents_f = 0.28
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ents_p = 0.0
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ents_r = 0.0
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ents_per_type = null
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[initialize]
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vectors = ${paths.vectors}
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.tokenizer]
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[initialize.components]
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[pretraining]
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